Currently, the most common method for inspecting essentially spherical objects (such as fruits and vegetables) involves production line personnel visually inspecting the objects as the objects are conveyed along a production line. However, the human visual inspection process is both slow and unreliable and some contaminating materials (such as fecal matter and bacterial contamination) that pose serious health risks are hard to identify particularly on a moving production line. Further, the inspected objects are not generally rotated so that all surfaces of the object are visible to the inspector.
To address these vulnerabilities, fruit and vegetable processors are developing machine vision systems to identify defects and contaminants. One example of such a system is disclosed in U.S. Pat. No. 7,787,111 to Kim et al. (hereinafter “Kim”), which is hereby incorporated by reference. The system disclosed by Kim comprises a rapid online line-scan imaging system capable of both hyperspectral/multispectral reflectance and fluorescence imaging. Reflectance imaging at multiple wavelengths detects quality and surface anomalies, while fluorescence imaging at multiple wavelengths is used to detect fecal matter and other types of bacterial contamination.
Although these examination tools and techniques improve the inspection process, the imaging systems are complex and expensive. For example, in accordance with Kim, multiple cameras may be required to adequately inspect all surfaces of a spheroid. Further, the data collected from all cameras must be processed and synchronized to accurately portray the three-dimensional spheroidal object. For maximum efficiency and minimal error, synchronization and processing should occur almost immediately to ensure that defective objects are not comingled with non-defective items.
The current invention simplifies the imaging process by providing an imaging system that utilizes only one camera and associated processor. The system described herein quickly and effectively gathers the imaging data and processes the data to produce a two-dimensional concatenated “image cube” that allows for the identification of essentially all surface defects as well as selected types of bacterial (including fecal) contamination.